This paper was originally published in the Journal of Economic Literature in December 2023.
Executive summary
Recent advances in generative AI have the potential to revolutionize research. Large language models (LLMs) have crossed the threshold to become useful across a wide range of cognitive tasks. This was illustrated by the viral reception of ChatGPT, which was released by OpenAI in November 2022, gained more than 100 million users in its first two months, and was soon estimated to produce a volume of text every 14 days that is equivalent to all of the printed works of humanity. OpenAI and Google DeepMind have since released even more powerful LLMs, GPT-4 and Gemini. Moreover, a growing number of established tech companies and startups have developed their own generative AI systems or adapted them to specific use cases in what some commentators have started to call a “Cambrian explosion” of large language models.
This article describes use cases of modern generative AI to interested economic researchers based on the author’s exploration of the space. The main emphasis is on LLMs, which are the type of generative AI that is currently most useful for research. I have categorized their use cases into six areas: ideation and feedback, writing, background research, data analysis, coding, and mathematical derivations. I provide general instructions for how to take advantage of each of these capabilities and demonstrate them using specific examples. Moreover, I classify the capabilities of the most commonly used LLMs from experimental to highly useful to provide an overview. My hope is that this paper will be a useful guide both for researchers starting to use generative AI and for expert users who are interested in new use cases beyond what they already have experience with to take advantage of the rapidly growing capabilities of LLMs. The online resources associated with this paper are available at the journal website and will provide semi-annual updates on the capabilities and use cases of the most advanced generative AI tools for economic research. In addition, they offer a guide on “How do I start?” as well as a page with “Useful Resources on Generative AI for Economists.”
Download the full paper here.
Click here for the online appendix and here for additional resources.
Authors
-
Acknowledgements and disclosures
Copyright American Economic Association; reproduced with permission.
The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.
